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| amazon business intelligence engineer: AWS Certified Enterprise IT Engineer , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| amazon business intelligence engineer: AWS certification guide - AWS Certified DevOps Engineer - Professional Cybellium Ltd, AWS Certification Guide - AWS Certified DevOps Engineer – Professional Master the Art of AWS DevOps at a Professional Level Embark on a comprehensive journey to mastering DevOps practices in the AWS ecosystem with this definitive guide for the AWS Certified DevOps Engineer – Professional certification. Tailored for DevOps professionals aiming to validate their expertise, this book is an invaluable resource for mastering the blend of operations and development on AWS. Within These Pages, You'll Discover: Advanced DevOps Techniques: Deep dive into the advanced practices of AWS DevOps, from infrastructure as code to automated scaling and management. Comprehensive Coverage of AWS Services: Explore the full range of AWS services relevant to DevOps, including their integration and optimization for efficient workflows. Practical, Real-World Scenarios: Engage with detailed case studies and practical examples that demonstrate effective DevOps strategies in action on AWS. Focused Exam Preparation: Get a thorough understanding of the exam structure, with in-depth chapters aligned with each domain of the certification exam, complemented by targeted practice questions. Written by a DevOps Veteran Authored by an experienced AWS DevOps Engineer, this guide marries practical field expertise with a deep understanding of AWS services, offering readers insider insights and proven strategies. Your Comprehensive Guide to DevOps Certification Whether you’re an experienced DevOps professional or looking to take your skills to the next level, this book is your comprehensive companion, guiding you through the complexities of AWS DevOps and preparing you for the Professional certification exam. Elevate Your DevOps Skills Go beyond the basics and gain a profound, practical understanding of DevOps practices in the AWS environment. This guide is more than a certification prep book; it's a blueprint for excelling in AWS DevOps at a professional level. Begin Your Advanced DevOps Journey Embark on your path to becoming a certified AWS DevOps Engineer – Professional. With this guide, you're not just preparing for an exam; you're advancing your career in the fast-evolving field of AWS DevOps. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| amazon business intelligence engineer: AWS Cloud Engineer Guide Sizwe Molefe, 2024-09-27 DESCRIPTION Cloud computing provides a more efficient, reliable, secure, and cost-effective way to run applications. Cloud computing offers customers access to rapidly growing amounts of data storage and computation resources while centralizing IT operations in the cloud provider's datacenter or in colocation data centers. Understand AWS basics such as EC2, VPCs, S3, and IAM while learning to design secure and scalable cloud architectures. This book guides you through automating infrastructure with CloudFormation and exploring advanced topics like containers, continuous integration and continuous delivery (CI/CD) pipelines, and cloud migration. You will also discover serverless computing with Lambda, API Gateway, and DynamoDB, enabling you to build efficient, modern applications. With real-world examples and best practices, this resource helps you optimize your AWS environment for both performance and cost, ensuring you can build and maintain robust cloud solutions. By the end of this book, you will be able to confidently design, build, and operate scalable and secure cloud solutions on AWS. Gain the expertise to leverage the full potential of cloud computing and drive innovation in your organization. KEY FEATURES ● Learn about AWS cloud in-depth with real-world examples and scenarios. ● Expand your understanding of serverless and containerization compute technology on AWS. ● Explore API’s along with API Gateway and its different use cases. WHAT YOU WILL LEARN ● How to get started with and launch EC2 instances. ● Working with and simplifying VPC’s, security groups, and network access control lists on AWS. ● Learn how to secure your AWS environment through the use of IAM roles and policies. ● Learn how to build scalable and fault-tolerant database systems using AWS database services such as RDS and Aurora. ● Learn how to set up a CI/CD pipeline on AWS. WHO THIS BOOK IS FOR Whether you are a system administrator, cloud architect, solutions architect, cloud engineer, DevOps engineer, security engineer, or cloud professional, this book provides valuable insights and practical guidance to help you build and operate robust cloud solutions on AWS. TABLE OF CONTENTS 1. Creating an AWS Environment 2. Amazon Elastic Compute Cloud 3. Amazon Virtual Private Cloud 4. Amazon S3: Simple Storage Service 5. Amazon API Gateway 6. AWS Database Services 7. Elastic Load Balancing and Auto Scaling 8. Amazon Route 53 9. Decouple Applications 10. CloudFormation 11. AWS Monitoring 12. AWS Security and Encryption 13. AWS Containers 14. Automating Deployments with CI/CD in AWS 15. AWS Cloud Migrations |
| amazon business intelligence engineer: Data Engineering with AWS Gareth Eagar, 2021-12-29 The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along. |
| amazon business intelligence engineer: Introduction to Engineering Dr. Darius Gnanaraj Solomon, 2021-07-21 The purpose of this e-book is to provide details about different disciplines of engineering to students who are planning to pursue a degree in engineering and help them to decide on a career in engineering. This book explores different disciplines of engineering and provides a broad background in each area. Basic concepts, as well as a few applications related to the following disciplines of Engineering, are presented in this book: Automobile/Aerospace Engineering, Civil Engineering, Computer Science & Engineering, Electrical and Electronics Engineering, Mechanical Engineering, and Production/Manufacturing Engineering. Illustrations are provided using colorful photographs having rich information. Details related to career opportunities and opportunities for further higher studies are available in this book. The authors hope that this book will help aspiring students of engineering programs to choose the discipline of their choice. |
| amazon business intelligence engineer: Proceedings of TEEM 2023 José Alexandre de Carvalho Gonçalves, |
| amazon business intelligence engineer: ⬆️ Amazon Web Services Certified (AWS Certified) Data Analytics Specialty (DAS-C01) Practice Tests Exams 83 Questions & Answers PDF Daniel Danielecki, 2023-11-01 ⌛️ Short and to the point; why should you buy the PDF with these Practice Tests Exams: 1. Always happy to answer your questions on Google Play Books and outside :) 2. Failed? Please submit a screenshot of your exam result and request a refund; we'll always accept it. 3. Learn about topics, such as: - Active Directory; - Amazon Athena; - Amazon Aurora; - Amazon CloudWatch; - Amazon DynamoDB; - Amazon Elastic Compute Cloud (Amazon EC2); - Amazon Elastic Map Reduce (Amazon EMR); - Apache Kafka; - Amazon Kinesis; - Amazon OpenSearch Service; - Amazon QuickSight; - Amazon Redshift; - Amazon Relational Database Service (Amazon RDS); - Amazon Simple Storage Service (Amazon S3); - Apache Spark; - AWS CloudFormation; - AWS Command Line Interface (AWS CLI); - AWS Glue; - AWS Identity and Access Management (AWS IAM); - AWS Key Management Service (AWS KMS); - AWS Lambda; - Extract, Transform, Load (ETL); - Hadoop Distributed File System (HDFS); - Input/Output operations Per Second (IOPS); - Virtual Private Clouds (VPC); - Much More! 4. Questions are similar to the actual exam, without duplications (like in other courses ;-)). 5. These tests are not an Amazon Web Services Certified (AWS Certified) Data Analytics Specialty (DAS-C01) Exam Dump. Some people use brain dumps or exam dumps, but that's absurd, which we don't practice. 6. 83 unique questions. |
| amazon business intelligence engineer: The Self-Taught Cloud Computing Engineer Dr. Logan Song, 2023-09-22 Transform into a cloud-savvy professional by mastering cloud technologies through hands-on projects and expert guidance, paving the way for a thriving cloud computing career Key Features Learn all about cloud computing at your own pace with this easy-to-follow guide Develop a well-rounded skill set, encompassing fundamentals, data, machine learning, and security Work on real-world industrial projects and business use cases, and chart a path for your personal cloud career advancement Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Self-Taught Cloud Computing Engineer is a comprehensive guide to mastering cloud computing concepts by building a broad and deep cloud knowledge base, developing hands-on cloud skills, and achieving professional cloud certifications. Even if you’re a beginner with a basic understanding of computer hardware and software, this book serves as the means to transition into a cloud computing career. Starting with the Amazon cloud, you’ll explore the fundamental AWS cloud services, then progress to advanced AWS cloud services in the domains of data, machine learning, and security. Next, you’ll build proficiency in Microsoft Azure Cloud and Google Cloud Platform (GCP) by examining the common attributes of the three clouds while distinguishing their unique features. You’ll further enhance your skills through practical experience on these platforms with real-life cloud project implementations. Finally, you’ll find expert guidance on cloud certifications and career development. By the end of this cloud computing book, you’ll have become a cloud-savvy professional well-versed in AWS, Azure, and GCP, ready to pursue cloud certifications to validate your skills.What you will learn Develop the core skills needed to work with cloud computing platforms such as AWS, Azure, and GCP Gain proficiency in compute, storage, and networking services across multi-cloud and hybrid-cloud environments Integrate cloud databases, big data, and machine learning services in multi-cloud environments Design and develop data pipelines, encompassing data ingestion, storage, processing, and visualization in the clouds Implement machine learning pipelines in a multi-cloud environment Secure cloud infrastructure ecosystems with advanced cloud security services Who this book is for Whether you're new to cloud computing or a seasoned professional looking to expand your expertise, this book is for anyone in the information technology domain who aspires to thrive in the realm of cloud computing. With this comprehensive roadmap, you’ll have the tools to build a successful cloud computing career. |
| amazon business intelligence engineer: Business Intelligence Marie-Aude Aufaure, Esteban Zimányi, 2012-01-11 Business Intelligence (BI) promises an organization the capability of collecting and analyzing internal and external data to generate knowledge and value, providing decision support at the strategic, tactical, and operational levels. Business Intelligence is now impacted by the Big Data phenomena and the evolution of society and users, and needs to take into account high-level semantics, reasoning about unstructured and structured data, and to provide a simplified access and better understanding of diverse BI tools accessible trough mobile devices. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. The lectures held at the First European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI technologies like data warehouses, OLAP query processing, or performance issues, but extend into new aspects that are important in this new environment and for novel applications, e.g., semantic technologies, social network analysis and graphs, services, large-scale management, or collaborative decision making. Combining papers by leading researchers in the field, this volume will equip the reader with the state-of-the-art background necessary for inventing the future of BI. It will also provide the reader with an excellent basis and many pointers for further research in this growing field. |
| amazon business intelligence engineer: Citizenship and Ethics Thomas A. Bryer, So Hee Jeon, 2021-04-03 Scholarship is a multi-generational collective enterprise with a commitment to advancing knowledge, inspiring reflection, and facilitating stronger neighborhoods, cities and countries. This book explicitly adopts this lens as a recognition of the contributions of Prof. Terry Cooper to scholarship and practice, and as a mechanism to connect the past to the present and ultimately the future of scholarship in public ethics and citizen engagement. This “multi-generational” approach is designed to reveal the persistent and future ongoing need to engage as a scholarly and practitioner community with these questions. The book is broken into three main sections: citizenship and neighborhood governance, public service ethics and citizenship, and global explorations of citizenship and ethics. Unique in this collection is the explicit linkage across the main focus areas of citizenship and ethics, as well as the comparative and global context in which these issues are explored. Cases and data are examined from the United States, Chile, Thailand, India, China, Georgia, and Myanmar. Ultimately, it is made clear through each individual chapter and the collective whole that research on citizenship and ethics within public affairs and service has a rich history, remains critical to the strengthening of public institutions today, and will only increase in global significance in the years ahead. |
| amazon business intelligence engineer: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| amazon business intelligence engineer: Infrastructure Monitoring with Amazon CloudWatch Ewere Diagboya, 2021-04-16 Explore real-world examples of issues with systems and find ways to resolve them using Amazon CloudWatch as a monitoring service Key FeaturesBecome well-versed with monitoring fundamentals such as understanding the building blocks and architecture of networkingLearn how to ensure your applications never face downtimeGet hands-on with observing serverless applications and servicesBook Description CloudWatch is Amazon's monitoring and observability service, designed to help those in the IT industry who are interested in optimizing resource utilization, visualizing operational health, and eventually increasing infrastructure performance. This book helps IT administrators, DevOps engineers, network engineers, and solutions architects to make optimum use of this cloud service for effective infrastructure productivity. You'll start with a brief introduction to monitoring and Amazon CloudWatch and its core functionalities. Next, you'll get to grips with CloudWatch features and their usability. Once the book has helped you develop your foundational knowledge of CloudWatch, you'll be able to build your practical skills in monitoring and alerting various Amazon Web Services, such as EC2, EBS, RDS, ECS, EKS, DynamoDB, AWS Lambda, and ELB, with the help of real-world use cases. As you progress, you'll also learn how to use CloudWatch to detect anomalous behavior, set alarms, visualize logs and metrics, define automated actions, and rapidly troubleshoot issues. Finally, the book will take you through monitoring AWS billing and costs. By the end of this book, you'll be capable of making decisions that enhance your infrastructure performance and maintain it at its peak. What you will learnUnderstand the meaning and importance of monitoringExplore the components of a basic monitoring systemUnderstand the functions of CloudWatch Logs, metrics, and dashboardsDiscover how to collect different types of metrics from EC2Configure Amazon EventBridge to integrate with different AWS servicesGet up to speed with the fundamentals of observability and the AWS services used for observabilityFind out about the role Infrastructure As Code (IaC) plays in monitoringGain insights into how billing works using different CloudWatch featuresWho this book is for This book is for developers, DevOps engineers, site reliability engineers, or any IT individual with hands-on intermediate-level experience in networking, cloud computing, and infrastructure management. A beginner-level understanding of AWS and application monitoring will also be helpful to grasp the concepts covered in the book more effectively. |
| amazon business intelligence engineer: Improving E-Commerce Web Applications Through Business Intelligence Techniques Sreedhar, G., 2018-02-02 As the Internet becomes increasingly interconnected with modern society, the transition to online business has developed into a prevalent form of commerce. While there exist various advantages and disadvantages to online business, it plays a major role in contemporary business methods. Improving E-Commerce Web Applications Through Business Intelligence Techniques provides emerging research on the core areas of e-commerce web applications. While highlighting the use of data mining, search engine optimization, and online marketing to advance online business, readers will learn how the role of online commerce is becoming more prevalent in modern business. This book is an important resource for vendors, website developers, online customers, and scholars seeking current research on the development and use of e-commerce. |
| amazon business intelligence engineer: Sustainable Development Through Data Analytics and Innovation Jorge Marx Gómez, Lawal O. Yesufu, 2022-09-26 Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis. The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management. |
| amazon business intelligence engineer: Data Science mit AWS Chris Fregly, Antje Barth, 2022-04-13 Von der ersten Idee bis zur konkreten Anwendung: Ihre Data-Science-Projekte in der AWS-Cloud realisieren Der US-Besteller zu Amazon Web Services jetzt auf Deutsch Beschreibt alle wichtigen Konzepte und die wichtigsten AWS-Dienste mit vielen Beispielen aus der Praxis Deckt den kompletten End-to-End-Prozess von der Entwicklung der Modelle bis zum ihrem konkreten Einsatz ab Mit Best Practices für alle Aspekte der Modellerstellung einschließlich Training, Deployment, Sicherheit und MLOps Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können. |
| amazon business intelligence engineer: Utilizing Big Data Paradigms for Business Intelligence Darmont, Jérôme, Loudcher, Sabine, 2018-08-10 Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five “Vs” of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence. |
| amazon business intelligence engineer: Data Analytics in the AWS Cloud Joe Minichino, 2023-04-06 A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS, accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform. |
| amazon business intelligence engineer: Driving Digital Transformation through Data and AI Alexander Borek, Nadine Prill, 2020-11-03 Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI. |
| amazon business intelligence engineer: Analytics Engineering with SQL and Dbt Rui Pedro Machado, Helder Russa, 2023-12-08 With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL. Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence. With this book, you'll learn: What dbt is and how a dbt project is structured How dbt fits into the data engineering and analytics worlds How to collaborate on building data models The main tools and architectures for building useful, functional data models How to fit dbt into data warehousing and laking architecture How to build tests for data transformations |
| amazon business intelligence engineer: Business Intelligence with Databricks SQL Vihag Gupta, 2022-09-16 Master critical skills needed to deploy and use Databricks SQL and elevate your BI from the warehouse to the lakehouse with confidence Key FeaturesLearn about business intelligence on the lakehouse with features and functions of Databricks SQLMake the most of Databricks SQL by getting to grips with the enablers of its data warehousing capabilitiesA unique approach to teaching concepts and techniques with follow-along scenarios on real datasetsBook Description In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics. This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You'll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You'll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you'll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse. By the end of this book, you'll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse. What you will learnUnderstand how Databricks SQL fits into the Databricks Lakehouse PlatformPerform everyday analytics with Databricks SQL Workbench and business intelligence toolsOrganize and catalog your data assetsProgram the data security model to protect and govern your dataTune SQL warehouses (computing clusters) for optimal query experienceTune the Delta Lake storage format for maximum query performanceDeliver extreme performance with the Photon query execution engineImplement advanced data ingestion patterns with Databricks SQLWho this book is for This book is for business intelligence practitioners, data warehouse administrators, and data engineers who are new to Databrick SQL and want to learn how to deliver high-quality insights unhindered by the scale of data or infrastructure. This book is also for anyone looking to study the advanced technologies that power Databricks SQL. Basic knowledge of data warehouses, SQL-based analytics, and ETL processes is recommended to effectively learn the concepts introduced in this book and appreciate the innovation behind the platform. |
| amazon business intelligence engineer: Computerworld , 2003-10-06 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| amazon business intelligence engineer: Research Handbook on E-Government Welch, Eric W., 2021-10-15 E-government is an increasingly well-established and wide-ranging field, in which there has been an explosion of new technologies, applications, and data resulting in new challenges and opportunities for e-government research and practice. This Research Handbook advances research in the field of e-government by first recognizing its roots and documenting its growth and progress. It investigates the advent and implications of new technologies, and structures the content around core topics of service, management, engagement and access. Two additional sections examine the role of e-government in developing countries and smart cities. |
| amazon business intelligence engineer: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| amazon business intelligence engineer: Competing on Analytics: Updated, with a New Introduction Thomas Davenport, Jeanne Harris, 2017-08-29 The New Edition of a Business Classic This landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition. Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company’s capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game. With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney’s vacation experience, Google’s HR, UPS’s logistics, the Chicago Cubs’ training methods, and Firewire Surfboards’ customization. Additional new topics and research include: Data scientists and what they do Big data and the changes it has wrought Hadoop and other open-source software for managing and analyzing data Data products—new products and services based on data and analytics Machine learning and other AI technologies The Internet of Things and its implications New computing architectures, including cloud computing Embedding analytics within operational systems Visual analytics The business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company’s fortunes in the age of analytics and big data. |
| amazon business intelligence engineer: Data Science and Analytics Strategy Kailash Awati, Alexander Scriven, 2023-04-05 This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls. |
| amazon business intelligence engineer: Azure Data Factory Cookbook Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton, 2024-02-28 Data Engineers guide to solve real-world problems encountered while building and transforming data pipelines using Azure's data integration tool Key Features Solve real-world data problems and create data-driven workflows with ease using Azure Data Factory Build an ADF pipeline that operates on pre-built ML model and Azure AI Get up and running with Fabric Data Explorer and extend ADF with Logic Apps and Azure functions Book DescriptionThis new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.What you will learn Build and Manage data pipelines with ease using the latest version of ADF Configure, load data, and operate data flows with Azure Synapse Get up and running with Fabric Data Factory Working with Azure Data Factory and Azure Purview Create big data pipelines using Databricks and Delta tables Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions Learn industry-grade best practices for using Azure Data Factory Who this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite. |
| amazon business intelligence engineer: Mastering Business Intelligence with MicroStrategy Dmitry Anoshin, Himani Rana, Ning Ma, 2016-07-29 Build world-class enterprise Business Intelligence solutions with MicroStrategy 10 About This Book Fix the gap between BI tools and implementation/integration processes with big data and predictive analytics using this comprehensive guide to MicroStrategy 10 Highly practical, example-rich guide that lets you implement business intelligence with MicroStrategy 10 in your organization Create the best user experience with BI dashboards using MicroStrategy using this up-to-date, comprehensive guide Who This Book Is For This book is intended for BI, DWH, ETL developers, BI/DWH/Analytics managers, analysts, and business users who already have MicroStrategy in their organization and want to take it to the next level in order to increase performance and improve user experience. In addition, it helps the reader to migrate from MicroStrategy 9 to MicroStrategy 10 and to start using the new capabilities. What You Will Learn Explore various visualization techniques for presenting analyzed data Customize MicroStrategy in order to meet your business requirements Develop and design mobile dashboards Use the advanced techniques such as designing reports, documents and interactive dashboards for building dashboards Understand the concepts of data discovery and Desktop capabilities Explore the best practices for Microstrategy system administration Find and fix issues based on connections, environment or documents Integrate third party ESRI map tools with MicroStrategy to create geo based reports In Detail Business intelligence is becoming more important by the day, with cloud offerings and mobile devices gaining wider acceptance and achieving better market penetration. MicroStrategy Reporting Suite is an absolute leader in the BI market and offers rich capabilities from basic data visualizations to predictive analytics. It lets you various delivery methods such as the Web, desktops, and mobiles. Using real-world BI scenarios, this book helps you to implement Business Analytics solutions in big e-commerce companies. It kicks off with MicroStrategy 10 features and then covers schema design models and techniques. Building upon your existing knowledge, the book will teach you advanced techniques for building documents and dashboards. It further teaches various graphical techniques for presenting data for analysis using maps, graphs, and advanced charts. Although MicroStrategy has rich functionality, the book will show how to customize it in order to meet your business requirements. You will also become familiar with the native analytical functions that will help you to maximize the impact of BI solutions with powerful predictive analytics. Furthermore, the book will focus on MicroStrategy Mobile Analytics along with data discovery and desktop capabilities such as connecting various data sources and building interactive dashboards. The book will also uncover best practices, troubleshooting techniques for MicroStrategy system administration, and also security and authentication techniques. Lastly, you will learn to use Hadoop for MicroStrategy reporting. By the end of the book, you will become proficient in evaluating any BI software in order to choose the best one that meets all business requirements. Style and approach This book will be focusing on providing extensive guide to plan how to design and develop complex BI architecture for real world scenario, using Microstrategy 10, best practices and collected experience working with BI, predictive analytics, and Microstrategy and big data. |
| amazon business intelligence engineer: Journey to Become a Google Cloud Machine Learning Engineer Dr. Logan Song, 2022-09-20 Prepare for the GCP ML certification exam along with exploring cloud computing and machine learning concepts and gaining Google Cloud ML skills Key FeaturesA comprehensive yet easy-to-follow Google Cloud machine learning study guideExplore full-spectrum and step-by-step practice examples to develop hands-on skillsRead through and learn from in-depth discussions of Google ML certification exam questionsBook Description This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer. The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional. The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together. The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate. What you will learnProvision Google Cloud services related to data science and machine learningProgram with the Python programming language and data science librariesUnderstand machine learning concepts and model development processesExplore deep learning concepts and neural networksBuild, train, and deploy ML models with Google BigQuery ML, Keras, and Google Cloud Vertex AIDiscover the Google Cloud ML Application Programming Interface (API)Prepare to achieve Google Cloud Professional Machine Learning Engineer certificationWho this book is for Anyone from the cloud computing, data analytics, and machine learning domains, such as cloud engineers, data scientists, data engineers, ML practitioners, and engineers, will be able to acquire the knowledge and skills and achieve the Google Cloud professional ML Engineer certification with this study guide. Basic knowledge of Google Cloud Platform and Python programming is required to get the most out of this book. |
| amazon business intelligence engineer: Effective Business Intelligence with QuickSight Rajesh Nadipalli, 2017-03-10 From data to actionable business insights using Amazon QuickSight! About This Book A practical hands-on guide to improving your business with the power of BI and Quicksight Immerse yourself with an end-to-end journey for effective analytics using QuickSight and related services Packed with real-world examples with Solution Architectures needed for a cloud-powered Business Intelligence service Who This Book Is For This book is for Business Intelligence architects, BI developers, Big Data architects, and IT executives who are looking to modernize their business intelligence architecture and deliver a fast, easy-to-use, cloud powered business intelligence service. What You Will Learn Steps to test drive QuickSight and see how it fits in AWS big data eco system Load data from various sources such as S3, RDS, Redshift, Athena, and SalesForce and visualize using QuickSight Understand how to prepare data using QuickSight without the need of an IT developer Build interactive charts, reports, dashboards, and storyboards using QuickSight Access QuickSight using the mobile application Architect and design for AWS Data Lake Solution, leveraging AWS hosted services Build a big data project with step-by-step instructions for data collection, cataloguing, and analysis Secure your data used for QuickSight from S3, RedShift, and RDS instances Manage users, access controls, and SPICE capacity In Detail Amazon QuickSight is the next-generation Business Intelligence (BI) cloud service that can help you build interactive visualizations on top of various data sources hosted on Amazon Cloud Infrastructure. QuickSight delivers responsive insights into big data and enables organizations to quickly democratize data visualizations and scale to hundreds of users at a fraction of the cost when compared to traditional BI tools. This book begins with an introduction to Amazon QuickSight, feature differentiators from traditional BI tools, and how it fits in the overall AWS big data ecosystem. With practical examples, you will find tips and techniques to load your data to AWS, prepare it, and finally visualize it using QuickSight. You will learn how to build interactive charts, reports, dashboards, and stories using QuickSight and share with others using just your browser and mobile app. The book also provides a blueprint to build a real-life big data project on top of AWS Data Lake Solution and demonstrates how to build a modern data lake on the cloud with governance, data catalog, and analysis. It reviews the current product shortcomings, features in the roadmap, and how to provide feedback to AWS. Grow your profits, improve your products, and beat your competitors. Style and approach This book takes a fast-paced, example-driven approach to demonstrate the power of QuickSight to improve your business' efficiency. Every chapter is accompanied with a use case that shows the practical implementation of the step being explained. |
| amazon business intelligence engineer: Python Business Intelligence Cookbook Robert Dempsey, 2015-12-22 Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions About This Book Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guide Learn to apply Python for business intelligence tasks—preparing, exploring, analyzing, visualizing and reporting—in order to make more informed business decisions using data at hand Learn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipes Who This Book Is For This book is intended for data analysts, managers, and executives with a basic knowledge of Python, who now want to use Python for their BI tasks. If you have a good knowledge and understanding of BI applications and have a “working” system in place, this book will enhance your toolbox. What You Will Learn Install Anaconda, MongoDB, and everything you need to get started with your data analysis Prepare data for analysis by querying cleaning and standardizing data Explore your data by creating a Pandas data frame from MongoDB Gain powerful insights, both statistical and predictive, to make informed business decisions Visualize your data by building dashboards and generating reports Create a complete data processing and business intelligence system In Detail The amount of data produced by businesses and devices is going nowhere but up. In this scenario, the major advantage of Python is that it's a general-purpose language and gives you a lot of flexibility in data structures. Python is an excellent tool for more specialized analysis tasks, and is powered with related libraries to process data streams, to visualize datasets, and to carry out scientific calculations. Using Python for business intelligence (BI) can help you solve tricky problems in one go. Rather than spending day after day scouring Internet forums for “how-to” information, here you'll find more than 60 recipes that take you through the entire process of creating actionable intelligence from your raw data, no matter what shape or form it's in. Within the first 30 minutes of opening this book, you'll learn how to use the latest in Python and NoSQL databases to glean insights from data just waiting to be exploited. We'll begin with a quick-fire introduction to Python for BI and show you what problems Python solves. From there, we move on to working with a predefined data set to extract data as per business requirements, using the Pandas library and MongoDB as our storage engine. Next, we will analyze data and perform transformations for BI with Python. Through this, you will gather insightful data that will help you make informed decisions for your business. The final part of the book will show you the most important task of BI—visualizing data by building stunning dashboards using Matplotlib, PyTables, and iPython Notebook. Style and approach This is a step-by-step guide to help you prepare, explore, analyze and report data, written in a conversational tone to make it easy to grasp. Whether you're new to BI or are looking for a better way to work, you'll find the knowledge and skills here to get your job done efficiently. |
| amazon business intelligence engineer: InfoWorld , 2003-10-06 InfoWorld is targeted to Senior IT professionals. Content is segmented into Channels and Topic Centers. InfoWorld also celebrates people, companies, and projects. |
| amazon business intelligence engineer: The Business Model Innovation Playbook Gennaro Cuofano, 2019-11-19 Business model innovation is about increasing the success of an organization with existing products and technologies by crafting a compelling value proposition able to propel a new business model to scale up customers and create a lasting competitive advantage. And it all starts by mastering the key customers. - The importance of business model innovation - Business model innovation enables you to create competitive moats - A multi-faceted concept - Analysts use business models to produce financial analyses - Academics study business models for the sake of classifying things - Most people confuse business models for business plans - Startups confuse business models for monetization strategies - Business model innovation is an experimentation mindset for entrepreneurs - An entrepreneur is not a scientist - Business model innovation is at the same time a mindset, a framework and a set of tools for entrepreneurs - Myth one: the best product wins - Myth two: technology is what gives a competitive advantage - Myth three: business model innovation is just about how you make money - What kind of questions do you need to ask with business model innovation? - Paths toward business model innovation - Engineer an innovative business model from scratch - Find an innovative business model along the way - Use business model innovation as a survival mechanism - Business model innovation examples - Netflix business model innovation (case study) - Amazon business model innovation (case study) - Apple business model innovation (case study) - Google business model innovation (case study) - Facebook business model innovation (case study) - Is business model innovation for anyone? - Key takeaways |
| amazon business intelligence engineer: Mastering Project Discovery Elliot Bendoly, Daniel Bachrach, Kathy Koontz, Porter Schermerhorn, 2024-04-11 Introducing a comprehensive approach to invigorate project leadership, this book provides a framework – the OUtCoMES Cycle – for developing, managing, advancing, and optimizing engineering and analytics projects. All too often, issues of moral hazard and completion bias prevent engineering and analytics managers and team leaders from asking the critical question 'What’s the problem?', before committing time, energy, and resources to solve it. This book draws attention to the definition, structuring, option consideration and ultimately the addressing of the right problems, exploring the OUtCoMES Cycle framework that facilitates and energizes systematic thinking, knowledge sharing, and on-the-fly adjustment with an explicit focus on the maximization of value and ROI. Each chapter includes discussions and lessons in analytical and engineering problem identification, problem structuring, iterative problem development (mental and computational) and problem resolution, at least three embedded real-world case studies, and a closing 'Practitioner’s Recap' to contextualize key chapter takeaways. Written by a team of established academic scholars and practicing analysts and engineers, this is an accessible and culture-shifting action guide for instructors interested in training the next generation of project and analytics leaders, students of analytics and engineering, as well as practicing project leaders and principals. |
| amazon business intelligence engineer: Unstructured Data Analytics Jean Paul Isson, 2018-03-02 Turn unstructured data into valuable business insight Unstructured Data Analytics provides an accessible, non-technical introduction to the analysis of unstructured data. Written by global experts in the analytics space, this book presents unstructured data analysis (UDA) concepts in a practical way, highlighting the broad scope of applications across industries, companies, and business functions. The discussion covers key aspects of UDA implementation, beginning with an explanation of the data and the information it provides, then moving into a holistic framework for implementation. Case studies show how real-world companies are leveraging UDA in security and customer management, and provide clear examples of both traditional business applications and newer, more innovative practices. Roughly 80 percent of today's data is unstructured in the form of emails, chats, social media, audio, and video. These data assets contain a wealth of valuable information that can be used to great advantage, but accessing that data in a meaningful way remains a challenge for many companies. This book provides the baseline knowledge and the practical understanding companies need to put this data to work. Supported by research with several industry leaders and packed with frontline stories from leading organizations such as Google, Amazon, Spotify, LinkedIn, Pfizer Manulife, AXA, Monster Worldwide, Under Armour, the Houston Rockets, DELL, IBM, and SAS Institute, this book provide a framework for building and implementing a successful UDA center of excellence. You will learn: How to increase Customer Acquisition and Customer Retention with UDA The Power of UDA for Fraud Detection and Prevention The Power of UDA in Human Capital Management & Human Resource The Power of UDA in Health Care and Medical Research The Power of UDA in National Security The Power of UDA in Legal Services The Power of UDA for product development The Power of UDA in Sports The future of UDA From small businesses to large multinational organizations, unstructured data provides the opportunity to gain consumer information straight from the source. Data is only as valuable as it is useful, and a robust, effective UDA strategy is the first step toward gaining the full advantage. Unstructured Data Analytics lays this space open for examination, and provides a solid framework for beginning meaningful analysis. |
| amazon business intelligence engineer: Handbook of Research on Technology Integration in the Global World Idemudia, Efosa C., 2018-07-27 Technology’s presence in society continues to increase as new products and programs emerge. As such, it is vital for various industries to rapidly adapt and learn to incorporate the latest technology applications and tools. The Handbook of Research on Technology Integration in the Global World is an essential reference source that examines a variety of approaches to integrating technology through technology diffusion, e-collaboration, and e-adoption. The book explores topics such as information systems agility, semantic web, and the digital divide. This publication is a valuable resource for academicians, practitioners, researchers, and upper-level graduate students. |
| amazon business intelligence engineer: Deep Learning By Example Ahmed Menshawy, 2018-02-28 Grasp the fundamental concepts of deep learning using Tensorflow in a hands-on manner Key Features Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples Book Description Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence. What you will learn Understand the fundamentals of deep learning and how it is different from machine learning Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning Increase the predictive power of your model using feature engineering Understand the basics of deep learning by solving a digit classification problem of MNIST Demonstrate face generation based on the CelebA database, a promising application of generative models Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation Who this book is for This book targets data scientists and machine learning developers who wish to get started with deep learning. If you know what deep learning is but are not quite sure of how to use it, this book will help you as well. An understanding of statistics and data science concepts is required. Some familiarity with Python programming will also be beneficial. |
| amazon business intelligence engineer: AWS Certified DevOps Engineer - Professional Certification and Beyond Adam Book, 2021-11-25 Explore the ins and outs of becoming an AWS certified DevOps professional engineer with the help of easy-to-follow practical examples and detailed explanations Key FeaturesDiscover how to implement and manage continuous delivery systems and methodologies on AWSExplore real-world scenarios and hands-on examples that will prepare you to take the DOP-C01 exam with confidenceLearn from enterprise DevOps scenarios to prepare fully for the AWS certification examBook Description The AWS Certified DevOps Engineer certification is one of the highest AWS credentials, vastly recognized in cloud computing or software development industries. This book is an extensive guide to helping you strengthen your DevOps skills as you work with your AWS workloads on a day-to-day basis. You'll begin by learning how to create and deploy a workload using the AWS code suite of tools, and then move on to adding monitoring and fault tolerance to your workload. You'll explore enterprise scenarios that'll help you to understand various AWS tools and services. This book is packed with detailed explanations of essential concepts to help you get to grips with the domains needed to pass the DevOps professional exam. As you advance, you'll delve into AWS with the help of hands-on examples and practice questions to gain a holistic understanding of the services covered in the AWS DevOps professional exam. Throughout the book, you'll find real-world scenarios that you can easily incorporate in your daily activities when working with AWS, making you a valuable asset for any organization. By the end of this AWS certification book, you'll have gained the knowledge needed to pass the AWS Certified DevOps Engineer exam, and be able to implement different techniques for delivering each service in real-world scenarios. What you will learnAutomate your pipelines, build phases, and deployments with AWS-native toolingDiscover how to implement logging and monitoring using AWS-native toolingGain a solid understanding of the services included in the AWS DevOps Professional examReinforce security practices on the AWS platform from an exam point of viewFind out how to automatically enforce standards and policies in AWS environmentsExplore AWS best practices and anti-patternsEnhance your core AWS skills with the help of exercises and practice testsWho this book is for This book is for AWS developers and SysOps administrators looking to advance their careers by achieving the highly sought-after DevOps Professional certification. Basic knowledge of AWS as well as its core services (EC2, S3, and RDS) is needed. Familiarity with DevOps concepts such as source control, monitoring, and logging, not necessarily in the AWS context, will be helpful. |
| amazon business intelligence engineer: Python Deep Learning Projects Matthew Lamons, Rahul Kumar, Abhishek Nagaraja, 2018-10-31 Insightful projects to master deep learning and neural network architectures using Python and Keras Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming |
| amazon business intelligence engineer: Learning AWK Programming Shiwang Kalkhanda, 2018-03-26 Text processing and pattern matching simplified Key Features -Master the fastest and most elegant big data munging language -Implement text processing and pattern matching using the advanced features of AWK and GAWK -Implement debugging and inter-process communication using GAWK Book Description AWK is one of the most primitive and powerful utilities which exists in all Unix and Unix-like distributions. It is used as a command-line utility when performing a basic text-processing operation, and as programming language when dealing with complex text-processing and mining tasks. With this book, you will have the required expertise to practice advanced AWK programming in real-life examples. The book starts off with an introduction to AWK essentials. You will then be introduced to regular expressions, AWK variables and constants, arrays and AWK functions and more. The book then delves deeper into more complex tasks, such as printing formatted output in AWK, control flow statements, GNU's implementation of AWK covering the advanced features of GNU AWK, such as network communication, debugging, and inter-process communication in the GAWK programming language which is not easily possible with AWK. By the end of this book, the reader will have worked on the practical implementation of text processing and pattern matching using AWK to perform routine tasks. What you will learn -Create and use different expressions and control flow statements in AWK -Use Regular Expressions with AWK for effective text-processing -Use built-in and user-defined variables to write AWK programs -Use redirections in AWK programs and create structured reports -Handle non-decimal input, 2-way inter-process communication with Gawk -Create small scripts to reformat data to match patterns and process texts Who this book is for This book is for developers or analysts who are inclined to learn how to do text processing and data extraction in a Unix-like environment. Basic understanding of Linux operating system and shell scripting will help you to get the most out of the book. |
| amazon business intelligence engineer: Generative AI with Amazon Bedrock Shikhar Kwatra, Bunny Kaushik, 2024-07-31 Become proficient in Amazon Bedrock by taking a hands-on approach to building and scaling generative AI solutions that are robust, secure, and compliant with ethical standards Key Features Learn the foundations of Amazon Bedrock from experienced AWS Machine Learning Specialist Architects Master the core techniques to develop and deploy several AI applications at scale Go beyond writing good prompting techniques and secure scalable frameworks by using advanced tips and tricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe concept of generative artificial intelligence has garnered widespread interest, with industries looking to leverage it to innovate and solve business problems. Amazon Bedrock, along with LangChain, simplifies the building and scaling of generative AI applications without needing to manage the infrastructure. Generative AI with Amazon Bedrock takes a practical approach to enabling you to accelerate the development and integration of several generative AI use cases in a seamless manner. You’ll explore techniques such as prompt engineering, retrieval augmentation, fine-tuning generative models, and orchestrating tasks using agents. The chapters take you through real-world scenarios and use cases such as text generation and summarization, image and code generation, and the creation of virtual assistants. The latter part of the book shows you how to effectively monitor and ensure security and privacy in Amazon Bedrock. By the end of this book, you’ll have gained a solid understanding of building and scaling generative AI apps using Amazon Bedrock, along with various architecture patterns and security best practices that will help you solve business problems and drive innovation in your organization.What you will learn Explore the generative AI landscape and foundation models in Amazon Bedrock Fine-tune generative models to improve their performance Explore several architecture patterns for different business use cases Gain insights into ethical AI practices, model governance, and risk mitigation strategies Enhance your skills in employing agents to develop intelligence and orchestrate tasks Monitor and understand metrics and Amazon Bedrock model response Explore various industrial use cases and architectures to solve real-world business problems using RAG Stay on top of architectural best practices and industry standards Who this book is for This book is for generalist application engineers, solution engineers and architects, technical managers, ML advocates, data engineers, and data scientists looking to either innovate within their organization or solve business use cases using generative AI. A basic understanding of AWS APIs and core AWS services for machine learning is expected. |
Business Intelligence Engineer I - Job ID: 2923272 - Amazon.jobs
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